This invention relates in general to a method for automatically locating instances of a specified image pattern in a digital image, and more specifically relates to a method for the automatic detection and classification of cylindrical bone patterns in digital projection radiographs.
Image processing algorithms that can automatically detect and classify patterns in digital imagery have broad application in consumer photography, remote sensing, and medical imaging. The present invention describes a flexible and computationally efficient approach for detecting and classifying patterns in digital imagery that has specific application to digital projection radiography. The method is useful for image visualization processing and computer aided diagnosis (CAD). The present invention has been optimized for the detection and classification of cylindrical bone patterns in digital projection radiographs.
Finding the location of the anatomical region-of-interest (ROI) in a digital projection radiograph is a critical step for enhancing the brightness, contrast and sharpness of an image. Once the ROI has been identified, standard techniques such as tone scaling and unsharp masking can be used to optimally render the image for diagnostic interpretation. The automatic segmentation of the bone and soft tissue anatomical regions is especially useful for generating image renderings that maximize the diagnostic information content that is presented to the radiologist for various types of exams. This processing must be fully automated to facilitate workflow in busy hospital imaging departments. Automatic detection of patterns in digital radiographs also has application to CAD. For example the automatic identification of certain bone patterns can be used as a pre-processing step for computer-assisted image interpretation. Once a specific bone pattern has been located, the bone morphology can be analyzed to obtain information about bone mineral density, bone growth, and fractures. The present invention has been optimized for locating the class of xe2x80x9ccylindricalxe2x80x9d bones, e.g. the humerus, femur, fingers, ribs, etc. However the technique is very flexible and may be generally applied to the detection of patterns in other image types such as for the automatic detection of cancerous masses and micro-calcifications in mammography. The technique is especially useful for higher dimensional spatial or spectral imagery, e.g., 3-dimensional CT or dual energy x-ray capture. In these applications, simple features can be used to first locate candidate regions which are then examined and classified by more detailed analysis.
Measurements of bone density have become an essential criterion for evaluating a patient""s risk of osteoporostic fracture. Commercially available instruments for measuring the bone mineral density (BMD) are divided into X-ray absorptiometry, quantitative computed tomography (QCT), and quantitative ultrasound (QUS). QCT allows the 3D visualization of trabecular microstructure and provides assessments beyond the basic BMD result, such as biomechanical parameters describing bone strength. However, QCT requires a CT scan and is an expensive procedure. Dual-energy X-ray absorptiometry (DXA) of the spine, femur, and total-body is a widely utilized method for predicting a patient""s risk of fracture. However, in many geographic areas there are inadequate resources to meet the demand, and DXA scans are not available to all patients who might benefit. Moreover, conventional DXA scanning is perceived as costly because of the need to refer patients to hospital-based facilities. Recently, there has been a wide variety of innovative equipment available for a small, low-cost dual-energy X-ray absorptiometry device dedicated to scanning the peripheral skeleton, for example, the forearm. As discussed in Christiansen et al. (C. Christiansen, P. Ravn, P. Alexandersen, and A. Mollgaard, xe2x80x9cA new region of interest (nROI) in the forearm for monitoring the effect of therapy,xe2x80x9d Journal of Bone Mineral Research, 12 (suppl 1): S480, 1997.), BMD measurements at forearm sites are well proven in predicting fracture risk. There is also a single-energy X-ray device, called radiographic absorptiometry (RA), where BMD in the hand (that is, the fingers) is assessed using a radiograph calibrated with an aluminum wedge. In the most recent development, devices designed to acquire a direct digital radiographic image of the hand enable bone density analysis to be performed in a physician""s office.
The challenge for computer aided diagnosis of bone disease is to position the region of interest for a highly precise measurement of BMD. Various methods for locating the bone region of interest have been proposed. For example, in order to use a posterior/anterior (PA) chest radiograph to analyze lung texture, the inter-rib bones shown in the PA chest image need to be removed. U.S. Pat. No. 4,851,984, xe2x80x9cMethod and system for localization of inter-rib spaces and automated lung texture analysis in digital chest radiographsxe2x80x9d, issued Jul. 25, 1989, to inventors K. Doi, et al., teaches a method to locate inter-rib spaces in digital chest radiograph images. First, a lung field is defined by determining the rib cage edge boundary. A horizontal signal profile is obtained at a predetermined vertical location. The pixel location at which the second derivative of this horizontal profile is minimum is defined as the rib cage edge boundary. Then two vertical profiles in the periphery of both lungs are fitted with a shift-invariant sinusoidal function. This technique assumes that the horizontal line is always perpendicular to the spinal column. Moreover, it assumes that the relative vertical locations of the objects are known a priori. Therefore, U.S. Pat. No. 4,851,984 does not teach a fully automatic method for locating instances of an image pattern. Furthermore, the profiles of the sinusoidal function do not accurately fit the profile of the cylindrical bone structure.
Histogram methods have been used to locate the bone region of interest. U.S. Pat. No. 5,228,068, xe2x80x9cDevice and method for automated determination and analysis of bone density and vertebral morphologyxe2x80x9d, issued Jul. 13, 1993, to inventor R. B. Mazess, teaches a method to determine and analyze vertebral morphology by evaluating the approximate center location of each vertebra from a digital lateral vertebral scan. The centers are located by evaluating the horizontal and vertical histograms. The horizontal histogram is constructed along a line which crosses each anterior-posterior border of the vertebra. The vertical histogram is obtained along a line crossing the superior-inferior border, which directs the spine column, because the patient is supported in the supine position on a table so that the vertebrae of the spine are generally aligned with the scan direction. However, because of the curvature of the spine, the angle of the vertebrae, that is, the angle of the anterior border, the posterior border, the superior border, and the inferior border with respect to the scan direction will vary among vertebrae. This method requires that this variation be accommodated by the trained eye of a physician in estimating the initial positions of lines which horizontal and vertical histogram are generated from. The rigid assumption about the relative orientation of the image relative to the body makes this technique sensitive to orientation error and can not be used in automatic mode for general image orientations.
Another histogram-based method disclosed in U.S. Pat. No. 4,951,201, issued Aug. 21, 1990, xe2x80x9cMethod of automatically determining imaged body posture in medical image displayxe2x80x9d, to inventors H. Takeo et al, is used to determine the image body posture. In order to produce an optimal visualization of the anatomy, the projection must be known. For example, for a chest image, the imaged thoracic vertebrae is of a relatively low density when it is imaged from the front side, and of relatively high density when it is imaged from the lateral side. This method determines imaged body posture by analyzing the histogram characteristics along a prescribed direction across the image. However, the method does not describe whether a prescribed direction is determined by a user interaction or by an automatic algorithm.
Other methods have used shape information to locate the bone region of interest. U.S. Pat. No. 4,922,915, xe2x80x9cAutomated image detail localization methodxe2x80x9d, issued May 8, 1990 to inventor B. A. Arnold, teaches a method to place a regular (e.g., elliptical) or irregular shape in a specific region of the image of the patient""s anatomy, such as the trabecular bone region of the patient""s spine. The cross-section image of an upper region of vertebra body contains the cortical bone image that appears as an outer ring, the trabecular bone image which occupies a portion of inside the ring, and the basivertebral vein image which occupies another small portion inside the ring. It is desired to exclude the basivertebrae vein from the trabecular bone image. This method requires that the operator position the enlarged region of interest so that it encompasses the top region of the vertebral body including the trabecular bone image but excluding the basivertebral vein image. Then the template search and histogram analysis algorithms are used to place the trabecular bone region.
Some researchers have proposed methods to segment images of the hand bone for the purpose of age assessment (see: e.g., (1) D. T. Morris and C. F. Walshaw, xe2x80x9cSegmentation of the finger bones as a prerequisite for the determination of bone age,xe2x80x9d Image and Vision Computing, Vol. 12, No. 4, pp. 239-246, May 1994; (2) E. Pietka, L. Kaabi, M. L. Kuo, and H. K. Huang, xe2x80x9cFeature extraction in carpal-bone analysis,xe2x80x9d IEEE Transactions on Medical Imaging, Vol. 12, No. 1, pp. 44-49, 1994; (3) E. Pietka, M. F. McNitt-Gray, M. L. Kuo, and H. K. Huang, xe2x80x9cComputer-assisted phalangeal analysis in skeletal age assessment,xe2x80x9d IEEE Transactions on Medical Imaging, Vol. 10, No. 4, pp. 616-619, December 1991 ;(4) C.-L. Chang, H.-P. Chan, L. T. Niklason, M. Cobby, J. Crabbe, and R. S. Adler, xe2x80x9cComputer-aided diagnosis: detection and characterization of hyperparathyroidism in digital hand radiographs,xe2x80x9d Medical Physics, 20(4), pp. 983-992, July/August 1993), and of the femur bone for the calculation of bone growth (see: U.S. Pat. No. 5,673,298, xe2x80x9cDevice and method for analysis of bone morphologyxe2x80x9d, issued Sep. 30, 1997, to inventor R. R. Mazess). In publications (1), (2) and (3) and in Mazess""s patent, the hand bone and the femur bone regions of interest were defined using a standard thresholding technique to separate the hand or femur from the background. Unfortunately, thresholding is not a robust and reliable image segmentation method for the texture-rich digital projection radiographic images. In Chang et al.""s publication (4), the extraction of the hand region of interest was performed manually. Then, an edge filter was applied to extract the edges of the finger bones. However, an edge filter usually produces numerous false alarms and misses, a complicated post-processing procedure is needed to remove these false alarms. The proposed method also relies on a human intervention, which is not desirable.
Eigenvector analysis, also known as principle component analysis, is a powerful statistical pattern classification method (see: K. Fukunaga, Introduction To Statistical Pattern Recognition, Academic Press, Inc., 1990; A. Mackiewicz and W. Ratajczak, xe2x80x9cPrincipal components analysis(PCA),xe2x80x9d Computers and Geosciences, Vol. 19, No. 3, pp. 303-342, March 1993.). The Eigenvector analysis method is a process of mapping the original measurements (e.g., the image space) into more effective features (e.g., a lower dimensional feature space), where the scatter of all mapped samples is maximized. Then, a classification criterion is designed based on a scatter measurement of the mapped coefficients. There are many applications of eigenvector analysis in the literature. The recent application of eigenvector methods to image analysis is found in face recognition (see: J. Zhang, Y. Yan, and M. Lades, xe2x80x9cFace recognition: eigenface, elastic matching and neural nets,xe2x80x9d Proceedings of the IEEE, Vol. 85, No. 9, pp. 1423-1435, September 1997; P. N. Belhuneur, J. P. Hespanha, and D. J. Kriegman, xe2x80x9cEigenfaces vs. fisherfaces: recognition using class specific linear projection,xe2x80x9d IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, July 1997; L. Sirovitch and M. Kirby, xe2x80x9cLow-dimensional procedure for the characterization of human faces,xe2x80x9d J. Optical Soc. of Am. A, vol. 2, pp. 519-524, 1987.). The eigenvectors of a covariance matrix of face vectors are generated to represent faces. Since the eigenvectors associated with the first few largest eigenvalues have face-like images, they also are referred to as eigenfaces. Finding the eigenvectors of the covariance matrix of an original image is difficult even for images of moderate size.
It is therefore desirable to provide a method which is simple, efficient and cost effective to solve these problems of known methods.
The present invention discloses a method for automatically locating instances of a specified pattern in a digital image using eigenvector analysis which solves the problems of known methods.
In general, the method effects extraction of certain image features first and then calculating the eigenvectors from this intermediate image feature space. According to a feature of the present invention there is provided a method comprising of providing a digital image from which simple features are detected that are associated with the specified pattern in the digital image. For each detected feature, a search is conducted in the spatial neighborhood of the detected feature for a second or a plural of other features associated with the target pattern. For each pair or plural of features detected, an eigenvector classifier is used to distinguish the wanted features from the unwanted features by matching the detected feature with the eigenvector representation that is constructed from a set of training profiles. The final step of the method is to label the image regions that are found to be consistent with the pattern in the classifying step.
The invention has the following advantages.
Optimal automatic rendering of an image to highlight for interpretation a specified region of interest or image pattern in a digital image is facilitated.
Computer assisted diagnosis, which requires image segmentation and bone morphology analysis in a digital medical image is facilitated.